Job Description: Forward Deployed AI Engineer – Agentic AI & Azure

Position: Forward Deployed AI Engineer (FDE)
Experience: 5–10+ years in software engineering, cloud architecture, or AI engineering
Location: Hybrid / Remote / Client-Site as Required
Employment Type: Full-Time

Role Overview

We are seeking a highly skilled, hands-on Forward Deployed AI Engineer to design, build, deploy, and operationalize enterprise-grade Agentic AI solutions within client environments.

This role combines deep technical engineering expertise with customer-facing solution delivery. The ideal candidate is comfortable working directly with business stakeholders, enterprise architects, and engineering teams to translate complex business problems into production-ready AI solutions.

The engineer will work across the entire AI solution lifecycle—from discovery and rapid prototyping to enterprise integration, deployment, monitoring, and continuous improvement—with a strong focus on Microsoft Azure, Generative AI, multi-agent orchestration, and enterprise automation.

Key Responsibilities1. Agentic AI Solution Engineering

- Design and implement autonomous and semi-autonomous AI agents capable of reasoning, planning, tool execution, and workflow orchestration.
- Develop multi-agent architectures using frameworks such as LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, or equivalent technologies.
- Build agentic workflows incorporating human-in-the-loop approvals, memory, state management, and dynamic task execution.
- Implement tool calling, function execution, Model Context Protocol (MCP), and agent-to-agent communication patterns.
- Develop enterprise Retrieval-Augmented Generation (RAG) solutions integrating structured and unstructured data.
- Implement agent evaluation, observability, guardrails, and continuous improvement mechanisms.

2. Microsoft Azure AI & Cloud Engineering

- Design, develop, and deploy AI applications using Azure AI Foundry / Microsoft Foundry, Azure OpenAI, and Azure AI Search.
- Build cloud-native AI services using Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure App Service.
- Implement secure integrations using Azure API Management, Azure Service Bus, Event Grid, and Logic Apps.
- Work with Azure Blob Storage, Azure SQL, Cosmos DB, and enterprise data platforms.
- Implement identity, security, and governance using Microsoft Entra ID, Managed Identities, Azure Key Vault, and Azure RBAC.
- Establish automated CI/CD pipelines using Azure DevOps or GitHub Actions.
- Monitor application reliability, performance, and cost using Azure Monitor, Application Insights, and OpenTelemetry.

3. Forward Deployed Engineering & Client Engagement

- Work directly with enterprise clients to identify high-value AI automation opportunities.
- Conduct technical discovery sessions, requirements workshops, and solution-design discussions.
- Rapidly prototype AI capabilities and demonstrate business value through working solutions.




- Embed within client engineering teams to accelerate implementation and adoption.
- Integrate agentic solutions with existing enterprise applications, APIs, databases, and business workflows.
- Troubleshoot complex production issues across AI models, application services, and cloud infrastructure.
- Translate business requirements into technical architecture, implementation plans, and measurable outcomes.
- Own solution delivery from proof of concept through production deployment and operational handover.

4. Enterprise AI Integration & Automation

- Integrate AI agents with enterprise platforms such as SAP, Salesforce, ServiceNow, Microsoft Dynamics, and custom applications.
- Develop API-based and event-driven integrations with existing enterprise systems.
- Design intelligent document-processing, decision-support, and workflow-automation solutions.
- Implement resilient agent execution with retries, exception handling, audit trails, and escalation workflows.
- Ensure AI-generated actions remain traceable, explainable, secure, and compliant with enterprise policies.

5. AI Engineering, Evaluation & Production Operations

- Develop production-quality applications primarily using Python and FastAPI.
- Build reusable agent components, connectors, orchestration services, and deployment templates.
- Implement automated testing for agent workflows, tools, prompts, and integrations.
- Establish evaluation frameworks covering accuracy, task completion, hallucination risk, latency, cost, and reliability.
- Apply LLMOps and DevSecOps practices for secure and scalable AI delivery.
- Optimize token usage, model selection, caching, and inference costs.
- Maintain technical documentation, architecture diagrams, and deployment runbooks.

Required Technical Skills

Programming & Backend Engineering

- Strong proficiency in Python, REST APIs, FastAPI, and asynchronous programming.
- Experience with microservices, distributed systems, and event-driven architectures.
- Robust understanding of SQL, NoSQL, Git, Docker, and automated testing.

Generative AI & Agentic Systems

- Hands-on experience building LLM-powered applications and AI agents.
- Knowledge of LangGraph, Semantic Kernel, Microsoft Agent Framework, or similar orchestration frameworks.
- Experience with RAG, vector databases, embeddings, tool calling, and prompt engineering.
- Understanding of multi-agent collaboration, planning, state management, and agent lifecycle management.
- Experience evaluating and debugging non-deterministic AI systems.

Microsoft Azure — Essential

- Azure OpenAI / Azure AI Foundry / Microsoft Foundry
- Azure AI Search




- Azure Functions / Container Apps / AKS
- Azure API Management
- Azure Storage / Cosmos DB / Azure SQL
- Microsoft Entra ID / Key Vault
- Azure DevOps / GitHub Actions
- Azure Monitor / Application Insights

Enterprise Architecture & Security

- API integration and enterprise authentication patterns.
- OAuth 2.0, OpenID Connect, managed identities, and RBAC.
- Secure handling of enterprise data and sensitive information.
- Familiarity with networking, private endpoints, and Azure landing-zone principles.

Preferred Qualifications

- Bachelor's or Master's degree in Computer Science, Engineering, AI, or a related discipline.
- 5+ years of software engineering experience, including hands-on Generative AI solution development.
- Experience delivering AI solutions into production within enterprise environments.
- Microsoft Azure certifications such as Azure AI Engineer Associate or Azure Solutions Architect Expert.
- Experience with Terraform, Bicep, or other Infrastructure-as-Code tools.
- Familiarity with enterprise AI governance, responsible AI, and regulatory compliance.
- Experience in financial services, insurance, healthcare, manufacturing, or other complex enterprise domains.
- Experience building agentic solutions involving document intelligence, enterprise knowledge retrieval, or workflow automation.

Critical Success Factors

The successful candidate will demonstrate:

- Hands-on execution: Ability to independently build, deploy, and troubleshoot working AI solutions—not just design architectures.
- Agentic AI depth: Practical understanding of how to engineer reliable, stateful, tool-using AI agents beyond basic chatbot implementations.
- Azure proficiency: Ability to deploy secure, scalable, production-ready AI workloads on Microsoft Azure.
- Client-facing ownership: Confidence engaging with business and technical stakeholders while working in ambiguous environments.
- Production mindset: Strong emphasis on observability, security, evaluation, maintainability, and operational reliability.
- Speed and adaptability: Ability to rapidly prototype, validate, iterate, and transition solutions into enterprise production environments.

What Makes This Role Different

This is not a traditional AI research, prompt-engineering, or solution-architecture-only role.

The Forward Deployed AI Engineer is expected to operate at the intersection of AI engineering, enterprise architecture, software development, and customer delivery.

The ideal candidate can sit with a client to understand a business problem in the morning, design an agentic solution, build a working prototype, integrate it with enterprise systems, and take ownership of its production deployment.

We are looking for builders who can turn Agentic AI concepts into measurable enterprise outcomes.

Pay: $90,800.00-$130,000.00 per year

Benefits:

- Casual dress
- Dental care
- Extended health care
- Paid time off
- Vision care

Work Location: Hybrid remote in Oshawa, ON L1J 7T4

📌 forward deployed engineer (Oshawa)
🏢 mirakalous technologies
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